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A comparative study of R functions for clustered data analysis [PDF]
Trials, 2021 Background Clustered or correlated outcome data is common in medical research studies, such as the analysis of national or international disease registries, or cluster-randomized trials, where groups of trial participants, instead of each trial ...
Wei Wang, Michael O. Harhay
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Using clustered data to develop biomass allometric models: The consequences of ignoring the clustered data structure. [PDF]
PLoS ONE, 2018 This paper investigates the consequences of ignoring the clustered data structure on allometric models. Clustered data, in the form of multiple trees sampled from multiple forest stands is commonly used to develop biomass allometric models.
Ioan Dutcă +3 more
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Camera Color Correction for Cultural Heritage Preservation Based on Clustered Data [PDF]
Journal of Imaging, 2021 Cultural heritage preservation is a crucial topic for our society. When dealing with fine art, color is a primary feature that encompasses much information related to the artwork’s conservation status and to the pigments’ composition.
Marco Trombini +4 more
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ROC Estimation from Clustered Data with an Application to Liver Cancer Data [PDF]
Cancer Informatics, 2016 In this article, we propose a regression model to compare the performances of different diagnostic methods having clustered ordinal test outcomes. The proposed model treats ordinal test outcomes (an ordinal categorical variable) as grouped-survival time ...
Joungyoun Kim +5 more
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Clustered Data Muling in the Internet of Things in Motion [PDF]
Sensors, 2019 This paper considers a case where an Unmanned Aerial Vehicle (UAV) is used to monitor an area of interest. The UAV is assisted by a Sensor Network (SN), which is deployed in the area such as a smart city or smart village.
Emmanuel Tuyishimire +2 more
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Entropy, 2022 Clinical risk prediction formulas for cancer patients can be improved by dynamically updating the formulas by intermediate events, such as tumor progression. The increased accessibility of individual patient data (IPD) from multiple studies has motivated
Takeshi Emura +2 more
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Stats, 2023 The area under the true ROC curve (AUC) is routinely used to determine how strongly a given model discriminates between the levels of a binary outcome. Standard inference with the AUC requires that outcomes be independent of each other.
Camden Bay +4 more
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Features of statistical analysis of quantitative data obtained from fellow eyes, nonparametric tests
Офтальмохирургия, 2022 Purpose. To compare various approaches to statistical analysis of fellow eyes and to describe correct analysis using nonparametric tests with R software. Material and methods. Various approaches to statistical analysis of fellow eyes are analyzed.
Ya.E. Pashentsev
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A New Estimator for Standard Errors with Few Unbalanced Clusters
Econometrics, 2022 In linear regression analysis, the estimator of the variance of the estimator of the regression coefficients should take into account the clustered nature of the data, if present, since using the standard textbook formula will in that case lead to a ...
Gianmaria Niccodemi, Tom Wansbeek
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CESER: An R Package to Compute Cluster Estimated Standard Errors
Journal of Open Research Software, 2021 This paper presents an implementation in R of the Cluster Estimated Standard Errors (CESE) proposed by [12]. The method estimates the covariance matrix of the estimated coefficients of linear models in grouped data sets with correlation among ...
Diogo Ferrari
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